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USING AIRBORNE LIDAR DATA FOR ASSESSMENT OF FOREST FIRE FUEL LOAD
POTENTIAL
M. İnana *, E. Bilicib, A.E. Akayb
a Istanbul University, Faculty of Forestry, Department of Surveying and Cadastre, Istanbul, Turkey, [email protected] b Bursa Technical University, Faculty of Forestry, 16310 Yıldırım, Bursa, Turkey, [email protected], [email protected]
KEY WORDS: LIDAR, Forestry, Voxel Point Cloud Segmentation, Forest Fire Risk, Fuel Load Potential
ABSTRACT:
Forest fire incidences are one of the most detrimental disasters that may cause long terms effects on forest ecosystems in many parts
of the world. In order to minimize environmental damages of fires on forest ecosystems, the forested areas with high fire risk should
be determined so that necessary precaution measurements can be implemented in those areas. Assessment of forest fire fuel load can
be used to estimate forest fire risk. In order to estimate fuel load capacity, forestry parameters such as number of trees, tree height,
tree diameter, crown diameter, and tree volume should be accurately measured. In recent years, with the advancements in remote
sensing technology, it is possible to use airborne LIDAR for data estimation of forestry parameters. In this study, the capabilities of
using LIDAR based point cloud data for assessment of the forest fuel load potential was investigated. The research area was chosen
in the Istanbul Bentler series of Bahceköy Forest Enterprise Directorate that composed of mixed deciduous forest structure.
* Corresponding author
1. INTRODUCTION
Natural disasters such as forest fires are increasing due to
climate change and human related factors. In Turkey, about
12,000 hectares of forested area has burned in more than 2,000
forest fires every year (Sivrikaya et al, 2014). The main factors
that affect the forest fire risk are meteorological factors,
topographical factors, and stand characteristics. Forest fuel
loads, estimated based on stand characteristics, are considered
to be another factor that differs from meteorological and
topographic factors because fuel loads are variable and
controllable (Küçük et al., 2005). Thus, estimation of fuel load
capacity is critical for forest management studies.
Stand characteristic related fire risk factors are generally
associated with many factors. These factors are fuel types,
canopy closure, fuel characteristics over the stages of stand
development, horizontal and vertical fuel (biomass) continuity,
terrain structure and underlying landform, and the distribution
of settlement and agricultural areas across the forest (Chuvieco,
et al., 1989; Scott et al., 2001; Bilgili, et al., 2003; Sağlam
et.al., 2008). The regression models can be developed to
determine canopy fuel loading potential based on stand
characteristics (Küçük et al, 2007). Remote sensing techniques
and Geographic Information Systems (GIS) have been also
used to determine fire risk (Chuvieco et al., 1989).
Arıcak et al., (2012) examined the availability and effectiveness
of satellite imagery that is one of the remote sensing techniques
in fighting forest fires. The fire potential mapping can be
generated by assessing and mapping general vegetation
complexes with the use of Landsat TM data and Geographic
Information System (Durmaz et al., 2006). Sivrikaya et al.
(2014) described and evaluated forest fire risk considering the
most important factors affecting fire behaviour at fine scales by
GIS. Yavuz et al. (2014) used remote sensing techniques to
evaluate the fuel load potential based on fuel moisture, wind
speed, and slope in Bayam Forest District by means of remote
sensing techniques.
In recent years, LiDAR technology has been used in the field of
forestry for the determination of forest types, forest
management, production planning, forest roads, and fire
behaviour modelling (Coulter et al., 2001; Akay and Sessions,
2005; Akay et al., 2009; Gülci et al., 2015; (Hopkinson et al.,
2016; Singh et al., 2016) indicated that LiDAR based forest
structure data and high-resolution DEMs can be used in wide
scale forestry activities such as stand characterizations, forest
inventory and management, fire behavior modelling, and forest
operations. Almeida et al., (2016) used a portable profiling
LiDAR for fire susceptibility and contrasting fire damage in the
Central Amazon. In this study, the use of LIDAR was evaluated
in the calculation of the forest fuel load with respect to forest
fire. The study was implemented in the Istanbul Bentler series
of Bahceköy Forest Enterprise Directorate.
2. MATERIAL AND METHODS
2.1 Study Area
The study area is a portion of Istanbul Northern Forest,
Bahceköy Forest Enterprise Directorate within the boarder of
İstanbul Regional Directorate of Forestry located in Turkey.
(Figure 1). In the Geographic Coordinate System, it is located
between 41˚ 13’ 31” – 41˚ 08’ 54” the northern latitude and 28˚
55' 59’’- 29 ˚ 01' 02" the east longitude.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October 2017, Safranbolu, Karabuk, Turkey
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W4-255-2017 | © Authors 2017. CC BY 4.0 License.
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Figure 1. Study Area
In Bentler region, the forest area is 3094 ha. The major trees in
the field are oak (Quercus spp.), beech (Fagus spp.), hornbeam
(Carpinus spp.), chestnut (Castenea spp.), alder (Alnus spp.),
ash (Fraxinus spp.), linden (Tilla spp.), Turkish red pine (Pinus
brutia), black pine (Pinus nigra), yellow pine (Pinus sylvestris),
nut pine (Pinus pinea), and maritime pine (Pinus pinaster).
Cover types dominated by oak and maritime pine were chosen
for this study because they are the dominant species and
represents the Istanbul’s natural forest structure in general.
2.2 Remote Sensing Data
Two data set used for this research (1) LiDAR, in the form of
LAS files that the data were collected in early October 2012; (2)
Pleiades image, which was taken on May 12 2014.
LiDAR data were collected with a RIEGL Q680i airborne
scanning system during leaf-on conditions by Bogaziçi Insaat
Mühendislik A.Ş. (BİMTAŞ). The flight altitude was 500 m
above ground level and the pulse density was 12 pulses per
meter in urban places and was 18 pulses per meter in forested
areas. The Pleiades image taken in 2014 that used to create a
color point cloud and for visualization. The used Pleiades data
were pansharpened with 4 bands (Blue: 0.422-0512 μm, Green:
0.5-0.584 μm, Red: 0.596-0.75 μm, NIR: 0.762-0894 μm) and
2.5 μm. In this study, LAStools ArcGis toolbox, ArcGIS 10.x,
Erdas Imagine 2015, CloudCompare V2.6.x software and some
codes which prepared in the Point Cloud Library software have
been used to determine each individually tree from the color
point cloud.
2.3 Field Data
The field data were collected in summer of 2012 and 2017. 286
plots measured randomly in 2012 and 100 plots measured in
2017.
Sampling in 2012 is traditional forest management
measurements and only diameter, height and species measured.
Additional work has been done in 2017 summer because the
crown diameter and the number of trees in the plot are required
for this study. There are 60 broad leaf forest type (oak and
beech stand) and 40 for the coniferous forest type (maritime
pine and black pine stand) in sampling of 2017. Plots in the
study area were distributed according to the forest cover type
and they were selected on purpose.
Oak stands cover 90% of study area. Topcon GRS1 DGPS
system was used to collect the coordinates at center of the plot.
Sampling areas were taken in 300 m2 size in accordance with
the traditional construction management regulations in order to
make future comparisons. In each plot, the woody species with
a diameter (DBH) ≥ 8 cm were measured using caliper (DBH)
from 1.30 m height and the tree length was measured using
Haglöf Vertex Laser. In addition, the number of trees and crown
diameter was recorded for each plot (Figure 2)
Figure 2. Field Study
2.4 Methods
386 sample areas were taken in this research in 2012 and 2017,
taking into account the tree type, age class. In this report, only
40 plot values were considered to determine the amount of fuel
load in oak areas that is the dominant species of the Bentler
series. In addition, volume parameters are taken as basis for fuel
load and the results are presented.
SPSS 22 program and Microsoft Office Excel program were
used for statistical analysis where average values and standard
deviations were computed, the relation between diameter,
crown and height was analysed by One-Way ANOVA at 0.05
confidence level. Pearson Correlation Test was used. Linear
Regression Analysis was used to determine mathematical
models for dependent and independent variables. Linear
Regression Analysis was used to determine mathematical
models for dependent and independent variables.
In this study, statistical relations between crown diameter, DBH
and height were investigated using plot survey data to determine
the amount of fire fuel load from the Lidar data. Because these
data can be obtained using Lidar data with high accuracy.
3. RESULT
A statistical analysis of the data was made from the study area
(Table 1). In the study, when measurements of diameter, height
and crown diameter of oak stands were statistically evaluated,
significant statistical relations were found between them (Figure
3). The formulas obtained as a result of the regression analysis
are noted on the graphs.
Min Max Mean Std. Deviation
Diameter 10,0 60,0 34,0 13,84
Crown Diameter 1,5 12,0 7,4 2,68
Height 11,0 27,2 19,03 4,03
Table 1. Descriptive Statistics
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October 2017, Safranbolu, Karabuk, Turkey
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W4-255-2017 | © Authors 2017. CC BY 4.0 License.
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Figure 3. a) DBH-Crown Diameter, b) DBH-Height for Oak
Aerial Lidar data and Pleiades image were combined in
ERDAS software (RGB Encode) to obtain a colored point
cloud. This data has been used in the elimination of tree
shadows in the future segmentation process.
Digital terrain model (DEM) and numerical surface models
(DSM) of the study area were obtained by using LasTools from
the new Lidar data set. These two new data sets were extracted
from each other to obtain a Canopy Height Model (CHM). The
obtained normalized data set is segmented by the codes
prepared in the PCL software and each detected tree treeID is
assigned. TreeIDs in the new vectorized data set also include
tabular data such as the tree height, crown diameter and crown
area of the single tree (Figure 4).
Figure 4. a. DSM, b. DEM, c. CHM, d. nCHM Profile,
e. Segmentation result with TreeID, f. Vectorize segmentation
result, g. Tabular data extract from LIDAR
Segmentation result is compared with the ground surveying data
in the study area and the segmentation parameters are regulated
until the highest accuracy is obtained. The highest accuracy
achieved for broad-leaved species in this study was 78.3%. In
order to estimate the volume of trees in the study area, the
volume increment tables in the forest management plans were
used. In the study area, the Crown diameter data generated from
the LIDAR data was transferred to the GIS database and DBH
was calculated for the trees with TreeID in the whole study area
by using linear regression formula (yDBH =
4.7535xCrownDia1.0449, R2 = 0.8492).
In the tree inventory studies in forestry, single (diameter-
volume) or double (diameter-height-volume) entry body
volume tables are generally used in the volume of sample areas
(Carus et al., 2014). This tabular data is associated with the
formula (yVol = 0,0002xDBH2,3884, R2 = 0,9994) generated from
the table in the management plans and is estimated in terms of
volume (m3) for each tree on the oak stand in the study area.
After this step, in order to obtain the volume of trees accepted
as fuel load throughout the study area, spatial analyses related
to the positions of the trees in the GIS were applied and
estimations were made for the areas between the two points.
For this purpose, the IDW (Inverse Distance Weighted)
technique was used to produce the thematic maps of the amount
of fuel load per hectares.
Figure 5. Volume and DBH thematic mapping for F21c053c
map sheet
The selected map sheet of F21c05c3c at 1/1000 scale located
within the boundaries of study area are shown at Figure 5. In
the figure, DBH and volume are produced in the thematic map
by IDW method in ArcGIS for volume as m3/ha and for DBH
as a cm/ha.
Finally, the thematic maps were evaluated for accuracy. For this
purpose, it was checked that whether the volume and DBH
values of randomly selected 250 trees were within the estimated
ranges that were not used in the prediction map. The tree values
of the verification points were taken from the prediction maps
and the differences between the actual values and the errors of
the smallest total of the black dots were calculated. The
accuracy of the generated positional maps was calculated as
RMSE 1.116.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October 2017, Safranbolu, Karabuk, Turkey
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W4-255-2017 | © Authors 2017. CC BY 4.0 License.
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4. CONCLUSIONS
Although LIDAR technology has very high potential for
different areas and commonly used in the world, it has very
limited usage in Turkey because it needs high technology and
equipment that brings high costs.
This technology, which has become more accessible in recent
years, emerges as a necessity to accelerate in the fields of
different uses in forestry studies. In this respect, it is important
to carry out studies to determine the use potential of LIDAR
data in forestry studies. In this study, the possibility of using
LIDAR data was evaluated in determining the amount of fuel
load in forest areas. It has been possible to produce spread maps
of the spatial distribution of the tree mass, considered as fuel
load, using the Aerial LIDAR data in oak area which is
dominant tree species areas of the Istanbul Bentler series. The
RMSE values of the thematic maps produced are found to be
1.116.
As a result, it is possible to estimate the dendrometric
parameters of forest stands with high accuracy by using Lidar
data alone or with a combination of different remote sensing
data. While these parameters can be used as fundamental data in
different forestry studies, it will be a preferable approach to
develop suitable methodologies for country forestry taking into
account other factors such as soil, growth environment, climate
in different forest organizations
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October 2017, Safranbolu, Karabuk, Turkey
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W4-255-2017 | © Authors 2017. CC BY 4.0 License.
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